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    "# Introduction to neuralHydrology\n",
    "The Python package `neuralHydrology` was was developed with a strong focus on research. The main application area is hydrology, however, in principle the code can be used with any data. To allow fast iteration of research ideas, we tried to develop the package as modular as possible so that new models, new data sets, new loss functions, new regularizations, new metrics etc. can be integrated with minor effort.\n",
    "\n",
    "There are two different ways to use this package:\n",
    "\n",
    "1. From the terminal, making use of some high-level entry points (such as `nh-run` and `nh-run-scheduler`)\n",
    "2. From any other Python file or Jupyter Notebook, using neuralHydrology's API\n",
    "\n",
    "In this tutorial, we will give a very short overview of the two different modes.\n",
    "\n",
    "Both approaches require a **configuration file**. These are `.yml` files which define the entire run configuration (such as data set, basins, data periods, model specifications, etc.). A full list of config arguments is listed in the [documentation](https://neuralhydrology.readthedocs.io/en/latest/usage/config.html) and we highly recommend to check this page and read the documentation carefully. There is a lot that you can do with this Python package and we can't cover everything in tutorials.\n",
    "\n",
    "For every run that you start, a new folder will be created. This folder is used to store the model and optimizer checkpoints, train data means/stds (needed for scaling during inference), tensorboard log file (can be used to monitor and compare training runs visually), validation results (optionally) and training progress figures (optionally, e.g., model predictions and observations for _n_ random basins). During inference, the evaluation results will also be stored in this directory (e.g., test period results).\n",
    "\n",
    "\n",
    "### TensorBoard logging\n",
    "By default, the training progress is logged in TensorBoard files (add `log_tensorboard: False` to the config to disable TensorBoard logging). If you installed a Python environment from one of our environment files, you have TensorBoard already installed. If not, you can install TensorBoard with:\n",
    "\n",
    "```\n",
    "pip install tensorboard\n",
    "``` \n",
    "\n",
    "To start the TensorBoard dashboard, run:\n",
    "\n",
    "```\n",
    "tensorboard --logdir /path/to/run-dir\n",
    "```\n",
    "\n",
    "You can also visualize multiple runs at once if you point the `--logdir` to the parent directory (useful for model intercomparison)\n",
    "\n",
    "### File logging\n",
    "In addition to TensorBoard, you will always find a file called `output.log` in the run directory. This file is a dump of the console output you see during training and evaluation.\n",
    "\n",
    "\n",
    "## Using `neuralHydrology` from the Terminal\n",
    "\n",
    "### nh-run\n",
    "\n",
    "\n",
    "Given a run configuration file, you can use the bash command `nh-run` to train/evaluate a model. To train a model, use\n",
    "\n",
    "\n",
    "```bash\n",
    "nh-run train --config-file path/to/config.yml\n",
    "```\n",
    "\n",
    "to evaluate the model after training, use\n",
    "\n",
    "```bash\n",
    "nh-run evaluate --run-dir path/to/run-directory\n",
    "```\n",
    "\n",
    "### nh-run-scheduler\n",
    "\n",
    "If you want to train/evaluate multiple models on different GPUs, you can use the `nh-run-scheduler`. This tool automatically distributes runs across GPUs and starts a new one, whenever one run finishes.\n",
    "\n",
    "Calling `nh-run-scheduler` in `train` mode will train one model for each `.yml` file in a directory (or its sub-directories).\n",
    "\n",
    "```bash\n",
    "nh-run-scheduler train --directory /path/to/config-dir --runs-per-gpu 2 --gpu_ids 0 1 2 3 \n",
    "```\n",
    "Use `-runs-per-gpu` to define the number of models that are simultaneously trained on a _single_ GPU (2 in this case) and `--gpu-ids` to define which GPUs will be used (numbers are ids according to nvidia-smi). In this example, 8 models will train simultaneously on 4 different GPUs.\n",
    "\n",
    "Calling `nh-run-scheduler` in `evaluate` mode will evaluate all models in all run directories in a given root directory.\n",
    "\n",
    "```bash\n",
    "nh-run-scheduler evaluate --directory /path/to/parent-run-dir/ --runs-per-gpu 2 --gpu_ids 0 1 2 3 \n",
    "```\n",
    "\n",
    "## API usage\n",
    "\n",
    "Besides the command line tools, you can also use the neuralHydrology package just like any other Python package by importing its modules, classes, or functions.\n",
    "\n",
    "This can be helpful for exploratory studies with trained models, but also if you want to use some of the functions or classes within a different codebase. \n",
    "\n",
    "Look at the [API Documentation](https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.html) for a full list of functions/classes you could use.\n",
    "\n",
    "The following example shows how to train and evaluate a model via the API."
   ]
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  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from neuralhydrology.evaluation import metrics\n",
    "from neuralhydrology.nh_run import start_run, eval_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a model for a single config file"
   ]
  },
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   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
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     "text": [
      "2021-06-04 10:12:08,990: Logging to /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208/output.log initialized.\n",
      "2021-06-04 10:12:08,991: ### Folder structure created at /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208\n",
      "2021-06-04 10:12:08,992: ### Run configurations for test_run\n",
      "2021-06-04 10:12:08,992: experiment_name: test_run\n",
      "2021-06-04 10:12:08,993: train_basin_file: 1_basin.txt\n",
      "2021-06-04 10:12:08,994: validation_basin_file: 1_basin.txt\n",
      "2021-06-04 10:12:08,995: test_basin_file: 1_basin.txt\n",
      "2021-06-04 10:12:08,996: train_start_date: 1999-10-01 00:00:00\n",
      "2021-06-04 10:12:08,996: train_end_date: 2008-09-30 00:00:00\n",
      "2021-06-04 10:12:08,997: validation_start_date: 1980-10-01 00:00:00\n",
      "2021-06-04 10:12:08,998: validation_end_date: 1989-09-30 00:00:00\n",
      "2021-06-04 10:12:08,998: test_start_date: 1989-10-01 00:00:00\n",
      "2021-06-04 10:12:08,999: test_end_date: 1999-09-30 00:00:00\n",
      "2021-06-04 10:12:09,000: device: cuda:0\n",
      "2021-06-04 10:12:09,000: validate_every: 3\n",
      "2021-06-04 10:12:09,001: validate_n_random_basins: 1\n",
      "2021-06-04 10:12:09,002: metrics: ['NSE']\n",
      "2021-06-04 10:12:09,004: model: cudalstm\n",
      "2021-06-04 10:12:09,007: head: regression\n",
      "2021-06-04 10:12:09,008: output_activation: linear\n",
      "2021-06-04 10:12:09,009: hidden_size: 20\n",
      "2021-06-04 10:12:09,010: initial_forget_bias: 3\n",
      "2021-06-04 10:12:09,011: output_dropout: 0.4\n",
      "2021-06-04 10:12:09,011: optimizer: Adam\n",
      "2021-06-04 10:12:09,012: loss: MSE\n",
      "2021-06-04 10:12:09,014: learning_rate: {0: 0.01, 30: 0.005, 40: 0.001}\n",
      "2021-06-04 10:12:09,014: batch_size: 256\n",
      "2021-06-04 10:12:09,015: epochs: 50\n",
      "2021-06-04 10:12:09,016: clip_gradient_norm: 1\n",
      "2021-06-04 10:12:09,017: predict_last_n: 1\n",
      "2021-06-04 10:12:09,018: seq_length: 365\n",
      "2021-06-04 10:12:09,018: num_workers: 8\n",
      "2021-06-04 10:12:09,019: log_interval: 5\n",
      "2021-06-04 10:12:09,020: log_tensorboard: True\n",
      "2021-06-04 10:12:09,021: log_n_figures: 1\n",
      "2021-06-04 10:12:09,022: save_weights_every: 1\n",
      "2021-06-04 10:12:09,023: dataset: camels_us\n",
      "2021-06-04 10:12:09,024: data_dir: /data/Hydrology/CAMELS_US\n",
      "2021-06-04 10:12:09,025: forcings: ['maurer', 'daymet', 'nldas']\n",
      "2021-06-04 10:12:09,026: dynamic_inputs: ['PRCP(mm/day)_nldas', 'PRCP(mm/day)_maurer', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet']\n",
      "2021-06-04 10:12:09,026: target_variables: ['QObs(mm/d)']\n",
      "2021-06-04 10:12:09,028: clip_targets_to_zero: ['QObs(mm/d)']\n",
      "2021-06-04 10:12:09,029: number_of_basins: 1\n",
      "2021-06-04 10:12:09,030: run_dir: /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208\n",
      "2021-06-04 10:12:09,031: train_dir: /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208/train_data\n",
      "2021-06-04 10:12:09,032: img_log_dir: /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208/img_log\n",
      "2021-06-04 10:12:09,068: ### Device cuda:0 will be used for training\n",
      "2021-06-04 10:12:11,786: Loading basin data into xarray data set.\n",
      "100%|██████████| 1/1 [00:00<00:00,  5.98it/s]\n",
      "2021-06-04 10:12:11,977: Create lookup table and convert to pytorch tensor\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.67s/it]\n",
      "# Epoch 1: 100%|██████████| 13/13 [00:00<00:00, 19.66it/s, Loss: 0.2051]\n",
      "2021-06-04 10:12:14,454: Epoch 1 average loss: 0.34159027613126314\n",
      "# Epoch 2: 100%|██████████| 13/13 [00:00<00:00, 20.80it/s, Loss: 0.1455]\n",
      "2021-06-04 10:12:15,089: Epoch 2 average loss: 0.2027114131129705\n",
      "# Epoch 3: 100%|██████████| 13/13 [00:00<00:00, 20.27it/s, Loss: 0.1797]\n",
      "2021-06-04 10:12:15,738: Epoch 3 average loss: 0.15873169440489548\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.41it/s]\n",
      "2021-06-04 10:12:16,870:  -- Median validation metrics:NSE: 0.65452\n",
      "# Epoch 4: 100%|██████████| 13/13 [00:00<00:00, 21.81it/s, Loss: 0.1534]\n",
      "2021-06-04 10:12:17,470: Epoch 4 average loss: 0.1346120943243687\n",
      "# Epoch 5: 100%|██████████| 13/13 [00:00<00:00, 20.66it/s, Loss: 0.1548]\n",
      "2021-06-04 10:12:18,107: Epoch 5 average loss: 0.1140809586414924\n",
      "# Epoch 6: 100%|██████████| 13/13 [00:00<00:00, 20.27it/s, Loss: 0.0947]\n",
      "2021-06-04 10:12:18,757: Epoch 6 average loss: 0.10437261829009423\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.56it/s]\n",
      "2021-06-04 10:12:19,243:  -- Median validation metrics:NSE: 0.71352\n",
      "# Epoch 7: 100%|██████████| 13/13 [00:00<00:00, 20.60it/s, Loss: 0.0758]\n",
      "2021-06-04 10:12:19,878: Epoch 7 average loss: 0.08874965315827957\n",
      "# Epoch 8: 100%|██████████| 13/13 [00:00<00:00, 19.76it/s, Loss: 0.0884]\n",
      "2021-06-04 10:12:20,545: Epoch 8 average loss: 0.09227672219276428\n",
      "# Epoch 9: 100%|██████████| 13/13 [00:00<00:00, 20.36it/s, Loss: 0.0557]\n",
      "2021-06-04 10:12:21,190: Epoch 9 average loss: 0.08220770439276329\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.58it/s]\n",
      "2021-06-04 10:12:21,667:  -- Median validation metrics:NSE: 0.71068\n",
      "# Epoch 10: 100%|██████████| 13/13 [00:00<00:00, 20.57it/s, Loss: 0.0887]\n",
      "2021-06-04 10:12:22,305: Epoch 10 average loss: 0.08383738879974072\n",
      "# Epoch 11: 100%|██████████| 13/13 [00:00<00:00, 19.50it/s, Loss: 0.1078]\n",
      "2021-06-04 10:12:22,978: Epoch 11 average loss: 0.08353882798781762\n",
      "# Epoch 12: 100%|██████████| 13/13 [00:00<00:00, 20.92it/s, Loss: 0.0700]\n",
      "2021-06-04 10:12:23,610: Epoch 12 average loss: 0.08276898557176957\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.69it/s]\n",
      "2021-06-04 10:12:24,085:  -- Median validation metrics:NSE: 0.75937\n",
      "# Epoch 13: 100%|██████████| 13/13 [00:00<00:00, 20.65it/s, Loss: 0.0521]\n",
      "2021-06-04 10:12:24,719: Epoch 13 average loss: 0.07291791244195057\n",
      "# Epoch 14: 100%|██████████| 13/13 [00:00<00:00, 20.61it/s, Loss: 0.0785]\n",
      "2021-06-04 10:12:25,356: Epoch 14 average loss: 0.06844786010109462\n",
      "# Epoch 15: 100%|██████████| 13/13 [00:00<00:00, 20.26it/s, Loss: 0.0888]\n",
      "2021-06-04 10:12:26,007: Epoch 15 average loss: 0.06409186869859695\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.79it/s]\n",
      "2021-06-04 10:12:26,478:  -- Median validation metrics:NSE: 0.75756\n",
      "# Epoch 16: 100%|██████████| 13/13 [00:00<00:00, 21.13it/s, Loss: 0.0557]\n",
      "2021-06-04 10:12:27,098: Epoch 16 average loss: 0.06648472954447453\n",
      "# Epoch 17: 100%|██████████| 13/13 [00:00<00:00, 19.32it/s, Loss: 0.0451]\n",
      "2021-06-04 10:12:27,777: Epoch 17 average loss: 0.05540313199162483\n",
      "# Epoch 18: 100%|██████████| 13/13 [00:00<00:00, 19.59it/s, Loss: 0.0911]\n",
      "2021-06-04 10:12:28,448: Epoch 18 average loss: 0.06357126511060275\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.91it/s]\n",
      "2021-06-04 10:12:28,919:  -- Median validation metrics:NSE: 0.74607\n",
      "# Epoch 19: 100%|██████████| 13/13 [00:00<00:00, 20.29it/s, Loss: 0.0485]\n",
      "2021-06-04 10:12:29,564: Epoch 19 average loss: 0.0661357558117463\n",
      "# Epoch 20: 100%|██████████| 13/13 [00:00<00:00, 19.36it/s, Loss: 0.0563]\n",
      "2021-06-04 10:12:30,244: Epoch 20 average loss: 0.06264124237574063\n",
      "# Epoch 21: 100%|██████████| 13/13 [00:00<00:00, 20.43it/s, Loss: 0.0284]\n",
      "2021-06-04 10:12:30,887: Epoch 21 average loss: 0.057722983022148795\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.77it/s]\n",
      "2021-06-04 10:12:31,373:  -- Median validation metrics:NSE: 0.76951\n",
      "# Epoch 22: 100%|██████████| 13/13 [00:00<00:00, 20.03it/s, Loss: 0.0454]\n",
      "2021-06-04 10:12:32,027: Epoch 22 average loss: 0.05375612727724589\n",
      "# Epoch 23: 100%|██████████| 13/13 [00:00<00:00, 20.19it/s, Loss: 0.0513]\n",
      "2021-06-04 10:12:32,679: Epoch 23 average loss: 0.05780608894733282\n",
      "# Epoch 24: 100%|██████████| 13/13 [00:00<00:00, 20.38it/s, Loss: 0.0627]\n",
      "2021-06-04 10:12:33,324: Epoch 24 average loss: 0.057307198930245176\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  6.05it/s]\n",
      "2021-06-04 10:12:33,806:  -- Median validation metrics:NSE: 0.76298\n",
      "# Epoch 25: 100%|██████████| 13/13 [00:00<00:00, 19.23it/s, Loss: 0.0401]\n",
      "2021-06-04 10:12:34,487: Epoch 25 average loss: 0.05840985648907148\n",
      "# Epoch 26: 100%|██████████| 13/13 [00:00<00:00, 19.62it/s, Loss: 0.0388]\n",
      "2021-06-04 10:12:35,158: Epoch 26 average loss: 0.05803900584578514\n",
      "# Epoch 27: 100%|██████████| 13/13 [00:00<00:00, 19.70it/s, Loss: 0.0388]\n",
      "2021-06-04 10:12:35,825: Epoch 27 average loss: 0.05348653002427174\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.93it/s]\n",
      "2021-06-04 10:12:36,285:  -- Median validation metrics:NSE: 0.77795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Epoch 28: 100%|██████████| 13/13 [00:00<00:00, 20.73it/s, Loss: 0.0597]\n",
      "2021-06-04 10:12:36,916: Epoch 28 average loss: 0.056730082545142904\n",
      "# Epoch 29: 100%|██████████| 13/13 [00:00<00:00, 21.20it/s, Loss: 0.0373]\n",
      "2021-06-04 10:12:37,536: Epoch 29 average loss: 0.05055128983580149\n",
      "2021-06-04 10:12:37,541: Setting learning rate to 0.005\n",
      "# Epoch 30: 100%|██████████| 13/13 [00:00<00:00, 20.56it/s, Loss: 0.1164]\n",
      "2021-06-04 10:12:38,177: Epoch 30 average loss: 0.054642367821473345\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.86it/s]\n",
      "2021-06-04 10:12:38,649:  -- Median validation metrics:NSE: 0.77841\n",
      "# Epoch 31: 100%|██████████| 13/13 [00:00<00:00, 20.67it/s, Loss: 0.0541]\n",
      "2021-06-04 10:12:39,283: Epoch 31 average loss: 0.04982277722312854\n",
      "# Epoch 32: 100%|██████████| 13/13 [00:00<00:00, 19.28it/s, Loss: 0.0782]\n",
      "2021-06-04 10:12:39,964: Epoch 32 average loss: 0.050953526909534745\n",
      "# Epoch 33: 100%|██████████| 13/13 [00:00<00:00, 20.27it/s, Loss: 0.0407]\n",
      "2021-06-04 10:12:40,611: Epoch 33 average loss: 0.041333115444733545\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  6.00it/s]\n",
      "2021-06-04 10:12:41,074:  -- Median validation metrics:NSE: 0.77428\n",
      "# Epoch 34: 100%|██████████| 13/13 [00:00<00:00, 20.37it/s, Loss: 0.0343]\n",
      "2021-06-04 10:12:41,718: Epoch 34 average loss: 0.04323964967177464\n",
      "# Epoch 35: 100%|██████████| 13/13 [00:00<00:00, 20.22it/s, Loss: 0.0414]\n",
      "2021-06-04 10:12:42,370: Epoch 35 average loss: 0.04770703069292582\n",
      "# Epoch 36: 100%|██████████| 13/13 [00:00<00:00, 19.64it/s, Loss: 0.0276]\n",
      "2021-06-04 10:12:43,040: Epoch 36 average loss: 0.047656144373692\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.91it/s]\n",
      "2021-06-04 10:12:43,519:  -- Median validation metrics:NSE: 0.80066\n",
      "# Epoch 37: 100%|██████████| 13/13 [00:00<00:00, 20.04it/s, Loss: 0.0334]\n",
      "2021-06-04 10:12:44,172: Epoch 37 average loss: 0.0458414600445674\n",
      "# Epoch 38: 100%|██████████| 13/13 [00:00<00:00, 19.43it/s, Loss: 0.0575]\n",
      "2021-06-04 10:12:44,848: Epoch 38 average loss: 0.04842810805600423\n",
      "# Epoch 39: 100%|██████████| 13/13 [00:00<00:00, 19.63it/s, Loss: 0.0445]\n",
      "2021-06-04 10:12:45,518: Epoch 39 average loss: 0.043286741352998294\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.37it/s]\n",
      "2021-06-04 10:12:46,014:  -- Median validation metrics:NSE: 0.77582\n",
      "2021-06-04 10:12:46,016: Setting learning rate to 0.001\n",
      "# Epoch 40: 100%|██████████| 13/13 [00:00<00:00, 19.15it/s, Loss: 0.0589]\n",
      "2021-06-04 10:12:46,702: Epoch 40 average loss: 0.05103066850167055\n",
      "# Epoch 41: 100%|██████████| 13/13 [00:00<00:00, 20.02it/s, Loss: 0.0473]\n",
      "2021-06-04 10:12:47,357: Epoch 41 average loss: 0.04762017182432688\n",
      "# Epoch 42: 100%|██████████| 13/13 [00:00<00:00, 19.81it/s, Loss: 0.0471]\n",
      "2021-06-04 10:12:48,022: Epoch 42 average loss: 0.04550328994026551\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.98it/s]\n",
      "2021-06-04 10:12:48,498:  -- Median validation metrics:NSE: 0.77417\n",
      "# Epoch 43: 100%|██████████| 13/13 [00:00<00:00, 19.95it/s, Loss: 0.0408]\n",
      "2021-06-04 10:12:49,154: Epoch 43 average loss: 0.041451460896776274\n",
      "# Epoch 44: 100%|██████████| 13/13 [00:00<00:00, 19.58it/s, Loss: 0.0483]\n",
      "2021-06-04 10:12:49,824: Epoch 44 average loss: 0.040512773709801525\n",
      "# Epoch 45: 100%|██████████| 13/13 [00:00<00:00, 20.20it/s, Loss: 0.0350]\n",
      "2021-06-04 10:12:50,477: Epoch 45 average loss: 0.04574364910905178\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.34it/s]\n",
      "2021-06-04 10:12:50,980:  -- Median validation metrics:NSE: 0.78605\n",
      "# Epoch 46: 100%|██████████| 13/13 [00:00<00:00, 20.51it/s, Loss: 0.0465]\n",
      "2021-06-04 10:12:51,619: Epoch 46 average loss: 0.04367495657732853\n",
      "# Epoch 47: 100%|██████████| 13/13 [00:00<00:00, 19.98it/s, Loss: 0.0387]\n",
      "2021-06-04 10:12:52,277: Epoch 47 average loss: 0.04123819476136795\n",
      "# Epoch 48: 100%|██████████| 13/13 [00:00<00:00, 19.21it/s, Loss: 0.0316]\n",
      "2021-06-04 10:12:52,961: Epoch 48 average loss: 0.042555324733257294\n",
      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  5.83it/s]\n",
      "2021-06-04 10:12:53,431:  -- Median validation metrics:NSE: 0.78377\n",
      "# Epoch 49: 100%|██████████| 13/13 [00:00<00:00, 19.29it/s, Loss: 0.0333]\n",
      "2021-06-04 10:12:54,109: Epoch 49 average loss: 0.04211612962759458\n",
      "# Epoch 50: 100%|██████████| 13/13 [00:00<00:00, 19.13it/s, Loss: 0.0398]\n",
      "2021-06-04 10:12:54,796: Epoch 50 average loss: 0.039138320546883806\n"
     ]
    }
   ],
   "source": [
    "start_run(config_file=Path(\"1_basin.yml\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate run on test set\n",
    "The run directory that needs to be specified for evaluation is printed in the output log above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-04 10:14:14,372: Using the model weights from /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208/model_epoch050.pt\n",
      "# Evaluation: 100%|██████████| 1/1 [00:00<00:00,  2.92it/s]\n",
      "2021-06-04 10:14:14,725: Stored results at /home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208/test/model_epoch050/test_results.p\n"
     ]
    }
   ],
   "source": [
    "run_dir = Path(\"/home/frederik/Projects/neuralhydrology/examples/01-Introduction/runs/test_run_0406_101208\")\n",
    "eval_run(run_dir=run_dir, period=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and inspect model predictions\n",
    "Next, we load the results file and compare the model predictions with observations. The results file is always a pickled dictionary with one key per basin (even for a single basin). The next-lower dictionary level is the temporal resolution of the predictions. In this case, we trained a model only on daily data ('1D'). Within the temporal resolution, the next-lower dictionary level are `xr`(an xarray Dataset that contains observations and predictions), as well as one key for each metric that was specified in the config file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['01022500'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(run_dir / \"test\" / \"model_epoch050\" / \"test_results.p\", \"rb\") as fp:\n",
    "    results = pickle.load(fp)\n",
    "    \n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data variables in the xarray Dataset are named according to the name of the target variables, with suffix `_obs` for the observations and suffix `_sim` for the simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (date: 3652, time_step: 1)\n",
       "Coordinates:\n",
       "  * date            (date) datetime64[ns] 1989-10-01 1989-10-02 ... 1999-09-30\n",
       "  * time_step       (time_step) int64 0\n",
       "Data variables:\n",
       "    QObs(mm/d)_obs  (date, time_step) float32 0.6203073 0.5536971 ... 0.9991529\n",
       "    QObs(mm/d)_sim  (date, time_step) float32 0.63002145 0.6063465 ... 1.2904731</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e5a53193-fafb-4d5b-aecc-3135580b0095' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5a53193-fafb-4d5b-aecc-3135580b0095' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 3652</li><li><span class='xr-has-index'>time_step</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b3dd7313-aea3-4e81-9095-c4c7f91b321e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3dd7313-aea3-4e81-9095-c4c7f91b321e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1989-10-01 ... 1999-09-30</div><input id='attrs-72bd8c28-0db6-4e23-9c4b-908384397aed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72bd8c28-0db6-4e23-9c4b-908384397aed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23de3255-48f3-458e-9088-b2b17c688c91' class='xr-var-data-in' type='checkbox'><label for='data-23de3255-48f3-458e-9088-b2b17c688c91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1989-10-01T00:00:00.000000000&#x27;, &#x27;1989-10-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-10-03T00:00:00.000000000&#x27;, ..., &#x27;1999-09-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-09-29T00:00:00.000000000&#x27;, &#x27;1999-09-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_step</span></div><div class='xr-var-dims'>(time_step)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-999f3981-008f-4ef0-bdf1-6b6fad27e5ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-999f3981-008f-4ef0-bdf1-6b6fad27e5ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c94348-a138-43c3-b059-84430ec5cd5a' class='xr-var-data-in' type='checkbox'><label for='data-a3c94348-a138-43c3-b059-84430ec5cd5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffbef5f1-0f85-47d4-ba1f-f8906e8e5daf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffbef5f1-0f85-47d4-ba1f-f8906e8e5daf' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>QObs(mm/d)_obs</span></div><div class='xr-var-dims'>(date, time_step)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6203073 0.5536971 ... 0.9991529</div><input id='attrs-8f9eccb0-9df3-44d3-9521-e0f89679b0ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f9eccb0-9df3-44d3-9521-e0f89679b0ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02da705d-a95d-4401-9829-6d7d1477c1b1' class='xr-var-data-in' type='checkbox'><label for='data-02da705d-a95d-4401-9829-6d7d1477c1b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.6203073],\n",
       "       [0.5536971],\n",
       "       [0.7118964],\n",
       "       ...,\n",
       "       [1.4529347],\n",
       "       [1.1823308],\n",
       "       [0.9991529]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QObs(mm/d)_sim</span></div><div class='xr-var-dims'>(date, time_step)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.63002145 0.6063465 ... 1.2904731</div><input id='attrs-f6f01228-f378-4a4c-bdff-8c5e6de51fa9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6f01228-f378-4a4c-bdff-8c5e6de51fa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5755cbcc-5f82-48a5-8c9d-be0a334cc0c9' class='xr-var-data-in' type='checkbox'><label for='data-5755cbcc-5f82-48a5-8c9d-be0a334cc0c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.63002145],\n",
       "       [0.6063465 ],\n",
       "       [0.9479629 ],\n",
       "       ...,\n",
       "       [2.0498252 ],\n",
       "       [1.583753  ],\n",
       "       [1.2904731 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f07fe0b-323d-40df-bbe9-c18879854c55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f07fe0b-323d-40df-bbe9-c18879854c55' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (date: 3652, time_step: 1)\n",
       "Coordinates:\n",
       "  * date            (date) datetime64[ns] 1989-10-01 1989-10-02 ... 1999-09-30\n",
       "  * time_step       (time_step) int64 0\n",
       "Data variables:\n",
       "    QObs(mm/d)_obs  (date, time_step) float32 0.6203073 0.5536971 ... 0.9991529\n",
       "    QObs(mm/d)_sim  (date, time_step) float32 0.63002145 0.6063465 ... 1.2904731"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['01022500']['1D']['xr']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the model predictions vs. the observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test period - NSE 0.804')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract observations and simulations\n",
    "qobs = results['01022500']['1D']['xr']['QObs(mm/d)_obs']\n",
    "qsim = results['01022500']['1D']['xr']['QObs(mm/d)_sim']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.plot(qobs['date'], qobs)\n",
    "ax.plot(qsim['date'], qsim)\n",
    "ax.set_ylabel(\"Discharge (mm/d)\")\n",
    "ax.set_title(f\"Test period - NSE {results['01022500']['1D']['NSE']:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to compute all metrics that are implemented in the neuralHydrology package. You will find additional hydrological signatures implemented in `neuralhydrology.evaluation.signatures`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NSE: 0.804\n",
      "MSE: 0.964\n",
      "RMSE: 0.982\n",
      "KGE: 0.847\n",
      "Alpha-NSE: 0.889\n",
      "Beta-NSE: 0.017\n",
      "Pearson-r: 0.897\n",
      "FHV: -12.015\n",
      "FMS: -5.833\n",
      "FLV: -178.330\n",
      "Peak-Timing: 0.174\n"
     ]
    }
   ],
   "source": [
    "values = metrics.calculate_all_metrics(qobs.isel(time_step=-1), qsim.isel(time_step=-1))\n",
    "for key, val in values.items():\n",
    "    print(f\"{key}: {val:.3f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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